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An airSea manta-ray robot in 5G OGCE 2019

An airSea manta-ray robot in 5G OGCE

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2nd international conference on Service Robotics Technologies, ICSRT 2019

作者： Lv, Zhi Wang, ZhongFeng Lv, Yi Yuan, MingZhe School of IT and Engineering Tianjin University of Technology and Education Tianjin300222 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781450362467

Based on the fifth generation of mobile communication (5G), 5G open grid computing environment (5G OGCE), in order to realize the exploitation and utilization of marine resources and energy, this paper presents an air and sea robot, which can fly and dive in the sea and air at the same time, and it is called an air and sea (airsea) Manta Ray robot. Its purpose is to carry out resources and energy exploitation and development under the support of 5G OGCE, and perform the tasks of different air and sea operation areas: (1) quickly implement underwater operations, operate in the underwater operation area during long voyage, monitor deep-sea oil exploitation, exploitation and utilization of seabed resources and energy, seabed rare earth and combustible ice mining and ocean current power generation;(2) fly over the ocean during long voyage to track the migration of Marine organisms and Marine migratory birds;(3) build manned air-sea vehicle that can act as the personnel and material transfer machine for the undersea space station. When undersea mining is carried out, it can complete adaptive control, and monitor the work of undersea underwater equipment. (4) Perform work and assignments in a large area of underwater and above water, as well as over the sea, in a swarm working mode and distributed working mode. By using the control-cloud architecture in 5G OGCE, the exploitation of seabed resources and the production of submarine intelligent factories centered on the submarine space station can be realized. © 2019 Association for computing Machinery.

关键词： Marine biology

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Autonomic machine learning for intelligent databases 2nd

Autonomic machine learning for intelligent databases

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2nd international conference on Applied Physics, System Science and Computers, APSAC2017

作者： Lee, Keon Myung Yoo, Jaesoo Hong, Jiman Department of Computer Science Chungbuk National University Cheongju Korea Republic of School of Information and Communication Engineering Chungbuk National University Cheongju Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of

ISBN: (纸本)9783319756042

Various machine learning algorithms are available off the shelf, even for free. It takes an expert to choose a proper algorithm for given task and to set hyperparameters of the algorithm. This paper addresses an architecture of autonomic machine learning platform with which developers get some assistance in choosing a machine learning algorithm appropriate to a task and in selecting the values of hyperparameters of the algorithm. Due to massive computation demands on executing machine learning algorithms, the platform is designed to utilize the external computing resources such as cloud computing systems and distributed computing systems. This paper presents the design choices and architecture of the proposed platform, and a possible application to intelligent databases. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Machine learning

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Economic Impact of Resource Optimisation in cloud Environment Using Different Virtual Machine Allocation Policies 2nd

Economic Impact of Resource Optimisation in Cloud Environmen...

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2nd international conference on Emerging Technologies in computing, iCETiC 2019

作者： Ahmad, Bilal Maroof, Zaib McClean, Sally Charles, Darryl Parr, Gerard School of Computing Ulster University Coleraine United Kingdom National Defence University Islamabad Pakistan University of East Anglia Norwich United Kingdom

ISBN: (纸本)9783030239428

Exceptional level of research work has been carried in the field of cloud and distributed systems for understanding their performance and reliability. Simulators are becoming popular for designing and testing different types of quality of service (QoS) matrices e.g. energy, virtualisation, and networking. A large amount of resource is wasted when servers are sitting idle which puts a negative impact on the financial aspects of companies. A popular approach used to overcome this problem is turning them ON/OFF. However, it takes time when they are turned ON affecting different matrices of QoS like energy consumption, latency, consumption and cost. In this paper, we present different energy models and their comparison with each other based on workloads for efficient server management. We introduce a different type of energy saving techniques (DVFs, IQRMC) which help toward an improvement in service. Different energy models are used with the same configuration and possible solutions are proposed for big data centres that are placed globally by large companies like Amazon, Giaki, Onlive, and Google. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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A Fog computing Framework for Quality of Service Optimisation in the Internet of Things (IoT) Ecosystem

A Fog Computing Framework for Quality of Service Optimisatio...

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international Multidisciplinary Information Technology and engineering conference (IMITEC)

作者： William Tichaona Vambe Khulumani Sibanda University of Fort Hare Alice South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” to support IoT devices that are resource-constrained. Although fog computing offers a lot of benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such challenge is the allocation of resources to IoT tasks to match their computational needs and QoS requirements whilst meeting task deadlines. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer. Of course, this helps in minimizing the communication delay, however, most tasks would end up missing their deadlines as many delays are experienced when fog node is deciding to process part of the task or offloading it to the next fog node. In this paper, we propose and introduce a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway whose goal is to decide where and when a task is to be offloaded either to the fog layer or the cloud layer based on their priority needs, computational needs and QoS requirements and minimize round-trip time. The study followed the four phases of the top-down methodology. To test the efficiency and effectiveness of the RAS, a model was evaluated in a simulated smart home setup. The important metrics that were used are the queuing time, offloading time and throughput. The results showed that RAS helps in minimizing the round-trip time, increase throughput and improve QoS. Furthermore, the approach addressed the starvation problem, which was affecting low priority tasks. Most importantly, the results provide evidence that if resource allocation and assignment are done properly, round-trip time (queuing time a

关键词： Ecosystems Quality of service Throughput Internet of Things Resource management Task analysis Edge computing

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Improved genetic algorithm for monitoring of virtual machines in cloud environment 2nd

Improved genetic algorithm for monitoring of virtual machine...

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2nd international conference on Smart computing and Informatics, SCI 2018

作者： Basu, Sayantani Kannayaram, G. Ramasubbareddy, Somula Venkatasubbaiah, C. School of Computer Science and Engineering VIT University VelloreTamil Nadu632 014 India

ISBN: (纸本)9789811319266

Resource utilization and energy need to be carefully handled for achieving virtualization in the cloud environment. An important aspect to be considered is that of load balancing, where the workload is distributed so that a particular node does not become overburdened with tasks. Improper load balancing will lead to losses in terms of both memory as well as energy consumption. The resources should be scheduled in a cloud in such a way that users obtain access at any time and with possibly less energy wastage. The proposed algorithm uses an improved Genetic Algorithm that helps reduce overall power consumption as well as performs scheduling of virtual machines so that the nodes are not loaded below or above their capacity. In this case, each chromosome in the population is considered to be a node. Each virtual machine is allocated to a node. The virtual machines on every node correspond to the genes of a chromosome. Crossover and mutation operations have been performed after which optimization techniques have been used to obtain the resulting allocation of tasks. The proposed approach has proved to be competent with respect to earlier approaches in terms of load balancing and resource utilization. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Energy utilization

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A parallel Adaptive DBSCAN Algorithm Based on k-Dimensional Tree Partition

A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional ...

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international conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Xin Lu Yu Wang Jiao Yuan Xun Wang Kun Fu Ke Yang School of software University of Electronic Science and Technology of China Chengdu China China Railway Eryuan Engineering Group Co. Ltd Chengdu China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The existing parallel DBSCAN (density based spatial clustering of applications with noise) algorithm needs to determine the parameter settings manually, and the datasets will be repeatedly accessed in the process of data partitioning and data merging, which reduces the efficiency of the algorithm excuting. Therefore, this paper proposes a parallel adaptive DBSCAN algorithm based on k-dimensional tree partition. It divides the dataset into several balanced data partitions by using k-dimensional tree, and carries out parallel computing in spark distributed computing framework, thus increasing the concurrent processing ability of the algorithm program and improving the I/O access speed. In addition, the improved adaptive DBSCAN parameter method is applied to each data partition for clustering analysis to obtain local clusters, which solves the random problem of manual setting parameters in the clustering process, and ensures the data quality of clustering mining. At the same time of creating local clusters, this algorithm also puts the mapping relationship between data points and adjacent points into the HashMap data structure of the master node, and uses it to merge local clusters into whole clusters, which can reduce the time cost of data merging. The experimental results show that the proposed algorithm can save about 18% running time compared with RDD-DBSCAN algorithm without reducing the clustering quality. With the increase of the number of cluster nodes, the running efficiency of the algorithm can be further improved, so it is suitable for processing massive data clustering analysis.

关键词： Machine learning algorithms Merging Clustering algorithms Data structures Partitioning algorithms Sparks Data mining

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Leader Election and Blockchain Algorithm in cloud Environment for E-Health

Leader Election and Blockchain Algorithm in Cloud Environmen...

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international conference on New Trends in computing Sciences (ICTCS)

作者： Basem Assiri Faculty of CS & IT Jazan University Jazan Saudi Arabia

The enhancement in e-health systems demands the adaptation of computerized techniques, new algorithms and methods. To achieve better efficiency, Electronic Personal Health Record (E-PHR) requires to adopt a new mode of storage such as cloud storage. cloud storage is a supportive technique that provides better security, easy availability and accessibility of files. However, the availability of E-PHR on the cloud allows parallel access to the corresponding files. In parallel and distributed computing, many users communicate and share resources to achieve a targeted goal. Therefore, leader election is a major technique that maintains and coordinates parallelism. This research implies the leader election algorithm for E-PHR in the cloud environment. It proposes an adoptive leader election algorithm (ALEA) that takes into account medical and healthcare specifications. Therefore, the paper has incorporated the ideas of Primary Leader, Secondary Leader for emergency, leader appointing and multiple tokens to allow parallel updates. ALEA limits the massage passing for leader electing and for acquiring token. In regular case it reduces the number massages to 0. Moreover, the paper discusses the advantages and disadvantages of using Blockchain technology to implement ALEA.

关键词： Medical services Voting cloud computing Bitcoin Complexity theory

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Adaptive Nature-inspired Fog Architecture 2

Adaptive Nature-inspired Fog Architecture

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2nd IEEE international conference on Fog and Edge computing (ICFEC)

作者： Kimovski, Dragi Ijaz, Humaira Saurabh, Nishant Prodan, Radu Univ Klagenfurt Distributed Multimedia Syst Grp Klagenfurt Austria Univ Innsbruck Distributed & Parallel Syst Grp Innsbruck Austria Univ Sargodha Sargodha Pakistan

ISBN: (纸本)9781538664889

During the last decade, cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature-inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi-criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

关键词： Computer architecture cloud computing Optimization Decision making Edge computing Adaptation models Electronic mail

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QoS-Aware Task Offloading Using Self-organized distributed cloudlet for Mobile cloud computing 2nd

QoS-Aware Task Offloading Using Self-organized Distributed C...

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2nd international conference on Computational Intelligence, Communication, and Business Analytics, CICBA 2018

作者： Guha Roy, Deepsubhra Ghosh, Ahona Mahato, Bipasha De, Debashis Centre of Mobile Cloud Computing Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology WB B.F.-142 Sector-I Salt Lake City KolkataWest Bengal700064 India

ISBN: (纸本)9789811385773

Edge-cloud has drawn a significant attention of the IT industries, cloud operators as well as the network service providers and mobile users. To meet the user requirements the service providers have been able to provide various types of services related to the network by applying virtual network functions and deployed cloudlet in Metropolitan Area Network. Our goal is to maximize the number of admitted requests via load balancing by reducing the integrated cost of admissions within a time spare. A problem function is designed to offload set of the tasks in a metropolitan area network, where a particular network related function with a maximum bearable delay is requested by each offloaded task through different network services according to different offloading requests. An algorithm is designed to make a reduced bipartite graph with maximum matching and minimum weight. To save cost we develop an effective prediction algorithm where instances of network functions get created and/or released in different cloudlets considering that the offloading request patterns dynamically changes. Later we have also analyzed that the possible outcome of the proposed algorithm is efficient while distributing the load among the cloudlets into the edge-cloud. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Metropolitan area networks

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2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

2nd EAI International Conference on Advanced Hybrid Informat...

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2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

ISBN: (纸本)9783030190859

The proceedings contain 64 papers. The special focus in this conference is on Advanced Hybrid Information processing. The topics include: Design and Implementation of Pedestrian Detection System;1090ES ADS-B Overlapped Signal Separation Research Based on Infomax Extension;real-Time Monitoring Technology of Potato Pests and Diseases in Northern Shaanxi Based on Hyperspectral Data;multi-mode Retrieval Method for Big Data of Economic Time Series Based on Machine Learning Theory;research on parallel Forecasting Model of Short-Term Power Load Big Data;approximate Data Fusion Algorithm for Internet of Things Based on Probability Distribution;An Adaptive Threshold VIRE Algorithm for Indoor Positioning;two-Dimensional Super-Resolution Direction Finding Algorithm for Wideband Chirp Signals;research on Data Synchronism Method in Heterogeneous Database Based on Web Service;automatic Calibration System of English Lesson Plan Information Under Big Data Analysis;research on the New Mode of Undergraduates’ Innovation and Entrepreneurship Education Under the Background of ‘Internet Plus’;design of Intelligent Driving System for Variable Speed Vehicle Based on Big Data Analysis;research on Trend Analysis Model of Movement Features Based on Big Data;research and Analysis on Comparison Scheme of IoT Encrypted Data in cloud computing Environment;DDoS Attack Detection Based on RBFNN in SDN;a Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection;a Method of Estimating Number of Signal with Small Snapshots;a Large-Scale Image Retrieval Method Based on Image Elimination Technology and Supervised Kernel Hash;an Improved Eigenvalue-Based Channelized Sub-band Spectrum Detection Method;research on Secure Storage of Multimedia Data Based on Block Chaining Technology.

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